Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "200" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 19 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 19 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459996 | RF_maintenance | 100.00% | 100.00% | 50.22% | 0.00% | - | - | 12.762284 | 39.350012 | 6.280765 | 0.139798 | 7.340323 | 5.146055 | 1.332017 | 6.510855 | 0.0407 | 0.2177 | 0.1406 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 100.00% | 58.32% | 0.00% | - | - | 13.051170 | 40.529225 | 5.441601 | -0.064820 | 8.147555 | 3.812975 | 0.962495 | 9.564434 | 0.0447 | 0.2132 | 0.1338 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 100.00% | 61.50% | 0.00% | - | - | 12.509677 | 39.380844 | 4.569022 | -0.071928 | 7.848981 | 4.489037 | 1.118426 | 6.975358 | 0.0414 | 0.2088 | 0.1354 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 100.00% | 63.48% | 0.00% | - | - | 14.817532 | 45.307696 | 4.351215 | -0.052182 | 9.264599 | 4.940924 | 0.738186 | 11.802055 | 0.0408 | 0.2040 | 0.1370 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 100.00% | 63.98% | 0.00% | - | - | 12.004336 | 36.854674 | 4.195260 | -0.061166 | 9.148935 | 5.004537 | 0.902395 | 16.433220 | 0.0429 | 0.2023 | 0.1332 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 100.00% | 65.05% | 0.00% | - | - | 11.773775 | 37.935389 | 3.712185 | 0.024811 | 8.070623 | 4.360419 | 0.466008 | 12.598235 | 0.0385 | 0.1987 | 0.1306 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 100.00% | 57.03% | 0.00% | - | - | 14.072443 | 44.359505 | 4.291212 | -0.114639 | 10.862105 | 7.071874 | 0.782058 | 11.355830 | 0.0401 | 0.2105 | 0.1407 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 100.00% | 64.45% | 0.00% | - | - | 11.684528 | 37.776006 | 4.301443 | -0.104836 | 6.458004 | 3.885554 | 2.229438 | 12.194998 | 0.0418 | 0.2140 | 0.1431 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 100.00% | 42.98% | 0.00% | - | - | 14.516310 | 44.543120 | 4.719492 | -0.111375 | 9.468850 | 5.096066 | 5.881672 | 11.987264 | 0.0407 | 0.2363 | 0.1581 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 100.00% | 65.03% | 0.00% | - | - | 13.360224 | 41.903499 | 4.420747 | -0.158631 | 7.296683 | 4.064723 | 2.089494 | 14.242494 | 0.0412 | 0.2060 | 0.1371 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 100.00% | 54.52% | 0.00% | - | - | 12.705522 | 39.122061 | 4.711912 | -0.080235 | 9.556535 | 7.584600 | 4.041964 | 5.494849 | 0.0411 | 0.2300 | 0.1541 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 100.00% | 50.73% | 0.00% | - | - | 12.498699 | 39.336137 | 4.298067 | 0.189765 | 9.366390 | 5.256011 | 3.300152 | 16.910054 | 0.0413 | 0.2378 | 0.1617 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 100.00% | 36.95% | 0.00% | - | - | 10.377070 | 30.654486 | 3.781452 | 1.310218 | 4.563912 | 2.884504 | 2.457411 | 3.849294 | 0.0411 | 0.2586 | 0.1717 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 100.00% | 72.83% | 0.00% | - | - | 11.653693 | 34.206957 | 4.400073 | 0.647279 | 10.548489 | 5.515448 | 1.063397 | 22.744504 | 0.0433 | 0.1845 | 0.1164 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 100.00% | 51.70% | 0.00% | - | - | 11.425949 | 32.459456 | 3.970694 | 0.608971 | 9.137712 | 5.499452 | 5.200297 | 6.666669 | 0.0425 | 0.2303 | 0.1499 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 100.00% | 76.85% | 0.00% | - | - | 11.838536 | 34.683840 | 3.495060 | 0.662452 | 9.043919 | 4.886131 | 1.120561 | 21.369942 | 0.0422 | 0.1769 | 0.1138 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 100.00% | 78.11% | 0.00% | - | - | 11.988810 | 35.073322 | 3.843564 | 0.729048 | 9.424871 | 5.533004 | 0.629902 | 23.027719 | 0.0388 | 0.1739 | 0.1111 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 100.00% | 73.17% | 0.00% | - | - | 12.261033 | 35.826490 | 3.997120 | 0.930195 | 9.346176 | 6.647551 | 1.383899 | 20.017320 | 0.0427 | 0.1811 | 0.1120 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 100.00% | 75.62% | 0.00% | - | - | 12.203354 | 35.180757 | 4.079468 | 0.838837 | 9.532930 | 4.687690 | 1.293562 | 19.083482 | 0.0406 | 0.1752 | 0.1104 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 39.350012 | 12.762284 | 39.350012 | 6.280765 | 0.139798 | 7.340323 | 5.146055 | 1.332017 | 6.510855 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 40.529225 | 13.051170 | 40.529225 | 5.441601 | -0.064820 | 8.147555 | 3.812975 | 0.962495 | 9.564434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 39.380844 | 12.509677 | 39.380844 | 4.569022 | -0.071928 | 7.848981 | 4.489037 | 1.118426 | 6.975358 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 45.307696 | 14.817532 | 45.307696 | 4.351215 | -0.052182 | 9.264599 | 4.940924 | 0.738186 | 11.802055 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 36.854674 | 36.854674 | 12.004336 | -0.061166 | 4.195260 | 5.004537 | 9.148935 | 16.433220 | 0.902395 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 37.935389 | 37.935389 | 11.773775 | 0.024811 | 3.712185 | 4.360419 | 8.070623 | 12.598235 | 0.466008 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 44.359505 | 44.359505 | 14.072443 | -0.114639 | 4.291212 | 7.071874 | 10.862105 | 11.355830 | 0.782058 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 37.776006 | 11.684528 | 37.776006 | 4.301443 | -0.104836 | 6.458004 | 3.885554 | 2.229438 | 12.194998 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 44.543120 | 44.543120 | 14.516310 | -0.111375 | 4.719492 | 5.096066 | 9.468850 | 11.987264 | 5.881672 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 41.903499 | 41.903499 | 13.360224 | -0.158631 | 4.420747 | 4.064723 | 7.296683 | 14.242494 | 2.089494 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 39.122061 | 12.705522 | 39.122061 | 4.711912 | -0.080235 | 9.556535 | 7.584600 | 4.041964 | 5.494849 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 39.336137 | 12.498699 | 39.336137 | 4.298067 | 0.189765 | 9.366390 | 5.256011 | 3.300152 | 16.910054 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 30.654486 | 10.377070 | 30.654486 | 3.781452 | 1.310218 | 4.563912 | 2.884504 | 2.457411 | 3.849294 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 34.206957 | 34.206957 | 11.653693 | 0.647279 | 4.400073 | 5.515448 | 10.548489 | 22.744504 | 1.063397 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 32.459456 | 32.459456 | 11.425949 | 0.608971 | 3.970694 | 5.499452 | 9.137712 | 6.666669 | 5.200297 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 34.683840 | 11.838536 | 34.683840 | 3.495060 | 0.662452 | 9.043919 | 4.886131 | 1.120561 | 21.369942 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 35.073322 | 35.073322 | 11.988810 | 0.729048 | 3.843564 | 5.533004 | 9.424871 | 23.027719 | 0.629902 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 35.826490 | 12.261033 | 35.826490 | 3.997120 | 0.930195 | 9.346176 | 6.647551 | 1.383899 | 20.017320 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 200 | N18 | RF_maintenance | nn Shape | 35.180757 | 35.180757 | 12.203354 | 0.838837 | 4.079468 | 4.687690 | 9.532930 | 19.083482 | 1.293562 |